Artificial Intelligence–Driven Radiomics in Otorhinolaryngology: Enhancing Diagnosis and Outcome Prediction in Head and Neck Cancer
Keywords:
artificial intelligence, radiomics, head and neck cancer, machine learning, outcome prediction, otorhinolaryngology, imagingAbstract
Recent advances in otorhinolaryngology have shifted toward data‑driven, imaging‑centric paradigms, with artificial intelligence (AI)‑integrated radiomics playing an increasingly central role in head and neck cancer management. AI‑based radiomics models extract quantitative features from computed tomography, magnetic resonance imaging, and positron‑emission tomography datasets to support noninvasive tumor characterization, treatment planning, and outcome prediction. This article reviews the integration of machine learning and deep‑learning radiomics into routine otolaryngology practice, compares conventional radiomics with emerging end‑to‑end deep‑learning approaches, and discusses practical implementation challenges and future directions. Data from recent studies indicate that combined clinical–radiomic models can improve prediction of survival, recurrence, and treatment response, while preserving or even surpassing the performance of conventional staging alone.
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